Since the LR-CNN is used to encode the important patch-based image representation into a feature-cube, therefore the main aim of the context block (CB) is to learn the spatial context within the feature cube. The CB learns the relation between the features of the image patches considering their spatial location. We propose three different CB architectures, each with different complexity and capability to capture the context information. First CB, \(\mathcal {B}_1(\cdot )\) , is comprised of a \(3\times 3\)  convolution layer followed by ReLU activation and batch normalization. Second CB, \(\mathcal {B}_2(\cdot )\) , uses residual block [1]} architecture with two different filter sizes. It consists of three convolution layers each followed by batch normalization and ReLU activation. The first and last layers are with \(1\times 1\)  convolution filter to squeeze and expand the feature depth. The output feature-maps of the last layer are concatenated with the input features-maps which makes its final output. The \(\mathcal {B}_2(\cdot )\)  is defined as:
\(\begin{aligned}\mathcal {B}_2(\textbf {F}^{\prime },\theta _{\mathcal {B}_2}) & = [L_{c}^{1\times 1}(\textbf {F}^{\prime },\theta _{\mathcal {B}^1_2}) \rightarrow L_{c}^{3\times 3}(\bullet ,\theta _{\mathcal {B}^2_2}) \\&\rightarrow L_{c}^{1\times 1}(\bullet ,\theta _{\mathcal {B}^3_2})] \oplus \textbf {F}^{\prime },\end{aligned}\) 
